In the management of systems of various sorts, such as various devices, plant equipment or network systems, it is necessary to promptly detect the occurrences of faults and to inquire into the causes of the faults as necessary. For this reason, a large variety of diagnostic devices that detect the faults and estimate the causes thereof based on data as measured from various parts of devices or systems of various sorts as the objects for diagnosis, have so far been proposed and put to practical use.
For example, Patent Document 1 has disclosed a diagnostic device for a sequential machine as an object for diagnosis. The diagnostic device holds in store a plurality of attribute values, indicating the normal operating conditions in each cycle of the sequential machine, as a criterion pattern. In actual management, a plurality of attribute values, indicating the operating conditions of the sequential machine for each machine cycle, are acquired to generate a pattern. This pattern is compared to a corresponding criterion pattern stored in a memory to detect a fault of the object being diagnosed. Patent Document 1 uses combinations of on/off operations of a plurality of limit switches that detect the movement of the sequential machine as a combination pattern of the values of a plurality of attributes.
Patent Document 2 discloses a diagnostic device in which, if such a fault has occurred that the value of a certain attribute in a time-series data of a plurality of attributes, as measured from an object for diagnosis, is offset from a criterion range, or if a user has specified an attribute subjected to a fault, a set or sets of attributes, the degree of correlation of which with the predetermined attributes subjected to the fault is higher than a predetermined criterion degree of correlation, is estimated to be the cause of the fault. More specifically, the degree of variations of the time series of a plurality of attributes with lapse of time is calculated based on the time-series data of the attributes in question. The degree of correlation, representing the intensity of correlation of the multiple attributes in question with other attributes, is calculated based on time-series data of the attributes in question and the other attributes. The set of the attributes, whose degree of correlation with the fault-related attributes is higher than a predetermined criterion degree of correlation, is output as the information indicating the cause of the fault.    [Patent Document 1] JP Patent Kokai Publication No. JP-A-59-218523    [Patent Document 2] JP Patent Kokai Publication No. JP-P2005-257416A    [Patent Document 3] JP Patent Kokai Publication No. JP-P2007-018530A    [Patent Document 4] JP Patent Kokai Publication No. JP-P2005-345154A    [Non-Patent Document 1] I. Takeuchi and K. Yamanishi, A unifying framework for detecting outliers and change points from time series, IEEE Transactions on Knowledge and Data Engineering, 18(4): 482-492, 2006    [Non-Patent Document 2] U. Lerner, Hybrid Bayesian Networks for Reasoning about Complex Systems, PHD thesis, Stanford University, 2002    [Non-Patent Document 3] M. M. Breuning, H. P. Kriegel, R. T. Ng, and J. Sander, LOF: Identifying density-based local outliers, In Proceedings of ACM SIGMOD Conference, ACM Press, 2000